926 P. C. Pototsky andW. Cresswell
TABLE 1 Number of peer-reviewed conservation science papers published during 1987–2017 that had at least one national author from any of the 41 sub-Saharan African countries, in ascending order (data retrieved from Clarivate Analytics, 2019; see text for search details).
Country Somalia
Djibouti Guinea Chad
Lesotho Comoros
Guinea-Bissau Eritrea Liberia
Mauritania Burundi
Republic of the Congo Gambia Angola
Sierra Leone
São Tomé & Príncipe Equatorial Guinea
Eswatini (formerly Swaziland) Togo
The Democratic Republic of the Congo Sudan Mali
Rwanda Niger
Côte d’Ivoire
Central African Republic Malawi Senegal
Mozambique Seychelles Zambia Gabon
Burkina Faso Botswana Benin
Namibia Ghana
Cameroon Zimbabwe Uganda Nigeria
Madagascar Tanzania Ethiopia Kenya
South Africa Total
No. of papers 1
5 7 9 9
11 13 14 14 15 16 19 19 24 26 37 38 42 42 44 53 58 65 69 82 84 93 93 97 99
117 123 144 172 188 188 258 349 385 400 451 459 677 686
1,290 4,986
12,701
by subsampling the total 12,701 articles. For each country, every fifth article was chosen systematically from the list of search results to create a random sample with respect to authorship. If an article did not concern conservation,
it was not sampled, and the preceding article was chosen. This was the case in ,10% of the subsampled papers. For this subsample, we extracted the publication title and
year,whether or not the primary authorwas fromthe country associatedwith the search and, if the primary authorwas a na- tional, the associated institution was noted. There is potential pseudo-replication in this sampling method: if a paper has authors from.1 sub-Saharan country or an author hasmul- tiple institutional affiliations, then a papermay be counted for more than one
country.However, if there were several authors from a country listed on a paper, a paper was only counted once for that country.We assumed that the proportion of such articles will be the same across countries, regardless of ab- solute number of articles or research institutions per country. The institution of the primary author was determined using the listed addresses, reprint address or institutional e-mail ad- dress. These options facilitated greater confidence in assigning an author’s geographical association, although some primary authors listed at non-African institutions were African au- thors working or studying abroad, which will lead to an over- attribution of research to non-nationals (Stocks et al., 2008), and underestimation of African publication output. In add- ition, some apparently national primary authors were proba- bly non-African researchers working at an African institu- tion, reducing the ratio of non-African to African primary authors (Stocks et al., 2008). Here we again assume that the proportion of such articles will be the same across countries.
Data analysis
Data were analysed using general linear modelling with a normal distribution of error residuals using R 3.5.2 (R Core Team, 2014). Firstly, the predictors of national con- servation research capacity across countries were explored using the full dataset. We used general linear modelling to explore any potential influence of the 14 country-specific variables (Table 1) on the total number of papers produced during 1987–2017 that had at least one national author. The number of research articles in all models was log trans- formed to obtain normally distributed residuals in the final models, as was population size, area, GDP and inter- national tourism. Next, the variables listed in Table 1 were used to construct a full model, containing all country- specific variables. No interactions between variables were considered so as not to over-parameterize the model. Before running the model, Pearson’s correlation tests were run to test for correlations between variables, with a thresh- old of 0.6 for removing highly correlated variables. Thus, political stability was removed from the full model be- cause of its high correlation with government effectiveness (Pearson’s correlation test: 0.71), and because political stability gave higher scores of Akaike’s information crite- rion (AIC) when substituted for government effectiveness in models.
Oryx, 2021, 55(6), 924–933 © The Author(s), 2020. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605320000046
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